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Browsing Computer Science, Engineering and Mathematics by Subject "Acoustic noise"
(Institute of Electrical and Electronics Engineers Computer Society (IEEE Publishing), 1996) Powers, David Martin; Dixon, Simon; Clark, Christopher Richard; Weber, Darren L
The paper deals with the problem of processing acoustic signals originating from multiple sources in a potentially noisy environment. Previous research in speech processing and cognitive modelling has tended to concentrate on single sources and relatively noise free signals. Separating out different signals from a multitude of sources is a significant part of human auditory processing. In speech processing research, the problem we are dealing with is known as the cocktail party syndrome. The processing of polyphonic music involves similar challenges, and auditory scene analysis (ASA) has been proposed as a means of separating out component signals and identifying their sources. In subliminal auditory processing, a speech signal which is masked from conscious awareness by a music signal provides an extreme form of the multiple source problem and permits exploration of the boundary between conscious and unconscious auditory processing. The research presented employs machine learning and associative models to characterize and track individual signals, and uses electroencephalographic (EEG) analysis to more precisely characterize human processing of multimodal signals.